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    Causal relationship exploration and applications

    日期:2019-03-28                   来源:                   作者:               关注:

    报告题目:Causal relationship exploration and applications

    报告人:Professor Jiuyong Li

    报告时间:2019年03月29日10:00

    报告地点:望江校区第三教学楼503会议室

    报告内容:Various machine learning methods make use of association relationships for classification and decision making. An association shows that two variables exhibit the same (or opposite) trend but may not indicate that the two variables have an inherent relationship. In other words, an association relationship can be spurious and/or conditional. Causal relationship discovery is to find the inherent relationships where the change of one variable leads to the change of another. The identification of casual relationships is crucial for understanding data and supports evidence based decision making. Causal discovery is a central task for science, health, economy and nearly all areas of studies. In this talk, I will discuss our work in the area and applications.

    报告人简介:Dr Jiuyong Li is a Professor and an Associate Head of School at the School of Information Technology and Mathematical Sciences of University of South Australia. He leads the Data Analytics Group in the School. His main research interests are in data mining, bioinformatics, and data privacy. His research work has been supported by Australian Research Council and Cooperative Research Centre for many years. He has published more than 150 papers, mostly in leading journals and conferences in the areas. He is a member of the Australian Computer Society National Committee for Artificial Intelligence. He has been a chair (or a PC chair) of multiple Australasian Data Mining and Artificial Intelligence conferences and actively serving PC (and senior PC) member for many international conferences in data mining. He has received senior visiting fellowships from Nokia Foundation, the Australian Academy of Science, and Japan Society of Promotion of Science.

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